Prediction System Research Articles

Development of an online prediction system for soil organic matter and soil moisture content based on multi-modal fusion

Realizing accurate determination of in situ soil organic matter (SOM) content and soil moisture (SM) content in the field is of great importance for improving agricultural production efficiency. However, the feature information of a single sensor is limited, and there is still a certain gap in modeling accuracy compared to traditional laboratory methods. The variations of SM in the field also interfere with data collection, limiting the application of single sensor. In order to realize the efficient detection of in-situ SOM and SM content, this study developed an online detection system for SOM and SM content based on the fusion of characteristic wavelengths, visible images and thermal imaging image features. Firstly, based on a vehicle-mounted platform, a visible-thermal imaging camera and a characteristic wavelength integration device were integrated in a deep pine plow to realize the simultaneous acquisition of in-situ soil multi-sensor data. Then, a lightweight multimodal network was constructed to obtain thermal visible light image features and characteristic wavelength features through branch networks, achieving deep fusion of different modal data and predicting SOM and SM content. Finally, output the forecasting results of SOM and SM content, and transmit the predictive information to the cloud platform for storage. It was verified that the Multi-modal proposed in this study worked best in the laboratory environment, with a predicted R2 of 0.91 and RMSE of 2.9 g/kg for SOM, and a predicted R2 of 0.92 and RMSE of 0.77 % for SM. Compared with single image or characteristic spectral data, the fusion of visible image and characteristic wavelength effectively improved the prediction accuracy of SOM. The real-time prediction of SM was realized by fusing thermal imaging data, and the elimination of the moisture effect of visible images and characteristic wavelengths was also realized with the help of deep fusion of Multi-modal network. After field validation, the R2 of the Multi-modal system was 0.84 and the RMSE was 5.0 g/kg for SOM, and the R2 of the SM was 0.88 and the RMSE was 1.03 %. Despite the differences in soil types between the validation field and the sampling field, the system still demonstrated strong generalization and achieved high accuracy prediction of in-situ SOM and SM in the field, which effectively improves the efficiency and applicability of field. The efficiency and applicability of soil testing effectively improve the efficiency and provide technical guidance for precision management in the field.

Drone motion prediction from flight data: a nonlinear time series approach

In this paper, we explore the application of data-driven predictive systems in enhancing unmanned aerial vehicle (UAV) control capabilities. We introduce a new model for predicting the motion of individual drones by utilizing fundamental flight control data. The model aims to improve the autonomy of individual drones and circumvent the complexity of traditional flight control systems, thus eliminating intricate nested controls. The proposed model lays the foundation for studying collective behaviours within a cluster of drones, thereby advancing the research into swarm behaviour exhibited by drones. The research findings demonstrate the potential of data-driven methods in the construction of UAV control systems. In particular, we here show a comparison of the prediction performances between two neural network architectures using real drone flight data involved in various kinds of motions. We explore the utility of using long short term memory (LSTM) and nonlinear autoregressive with exogenous inputs (NARX) family of nonlinear time series models in developing a virtual drone model using real experimental data.

Election Voting Trend Prediction System

This paper presents the use of Bayesian networks and K-Nearest Neighbor algorithms for predicting election results. Our motivation stemmed from the complexities of the election data available, which spans over 120,000 voting locations across 36 states in Nigeria, and the requirement to develop a procedure that takes into consideration voter trends that are influenced by political parties seeking to win. The system architecture's translation was utilised, and the prototyping methodology was adopted. In order to realize the requirements, the system was designed and implemented using Java and MySQL in accordance with specifications. Since the outcome is positive, it can serve as a benchmark for further study in this field, particularly when it comes to using data mining tools to analyze election results.

Development of an expert system for technological support of required roughness when machining hardened steel parts on numerically controlled machines

This work addresses the problem of improving the surface quality of parts of rotation bodies, which are made of hardened steels, in the course of their machining on lathes with numerical program control. The research object was the surface roughness of parts. The methodology involved system analysis and synthesis, artificial neural networks, fuzzy logic, experiment, and processing of experimental results. A decomposition scheme for the expert system structure was developed, which can be used as the basis for formulating requirements to a future expert system for monitoring and prediction of roughness parameters. It was established that the models currently applied to describe the relationship between surface quality and the technological regimes used to ensure the technological level of roughness give a high error of over 20%. The possibility of using models that apply artificial intelligence and contain neural network blocks and decision-making devices based on fuzzy logic is substantiated. It is shown that such a combination makes it possible to customize the system for processing parts of a certain production range, as well as a more correct assessment of the onset of catastrophic wear of cutting tools. The neuro-fuzzy model was confirmed to have an error of less than 10%, which is significantly lower than when using spectral or correlation models. According to the testing results, the proposed expert system for monitoring and prediction of roughness parameters enables a 2.5-fold reduction in the scatter of roughness parameters under an increase in tool wear, compared to without its application. Thus, the proposed system makes it possible to assess the level of cutting tool wear more correctly and determine the onset of its limit state.

Introduction of a free sound system design exchange format (SDE) for data from different sound systems for noise predictions with complex summation

To consider the correlated sources of sound systems from different manufacturers in noise predictions equally with complex summation, a free exchange format for sound system planning software on the one hand and environmental noise software on the other is being defined in a cooperation with d&b audiotechnik, L-Acoustics and SoundPLAN: SDE = Sound/System Design/Data Exchange/Export This paper introduces the SDE format, its motivation and structure. With increasing open-air events in urban areas, potential problems with noise also increase. Proactive noise predictions can help prevent noise from becoming a problem in the first place. Until now it has not been possible to use data from planned sound systems from different manufacturers for equivalent calculations with complex summation. Modern sound systems have high directivity, especially loudspeaker arrays. The directivity of arrays results from superposition of sound waves from all loudspeakers. This effect is used specifically with line arrays and subwoofer arrays to optimize sound and coverage in audience areas. However, the resulting emission of an entire sound system is not constant over distance. Time-of-flight and relative phase at the receiver points must be considered with complex summation.

Anticipating multisensory environments: Evidence for a supra-modal predictive system

Our perceptual experience is generally framed in multisensory environments abundant in predictive information. Previous research on statistical learning has shown that humans can learn regularities in different sensory modalities in parallel, but it has not yet determined whether multisensory predictions are generated through a modality-specific predictive mechanism or instead, rely on a supra-modal predictive system. Here, across two experiments, we tested these hypotheses by presenting participants with concurrent pairs of predictable auditory and visual low-level stimuli (i.e., tones and gratings). In different experimental blocks, participants had to attend the stimuli in one modality while ignoring stimuli from the other sensory modality (distractors), and perform a perceptual discrimination task on the second stimulus of the attended modality (targets). Orthogonal to the task goal, both the attended and unattended pairs followed transitional probabilities, so targets and distractors could be expected or unexpected. We found that participants performed better for expected compared to unexpected targets. This effect generalized to the distractors but only when relevant targets were expected. Such interactive effects suggest that predictions may be gated by a supra-modal system with shared resources across sensory modalities that are distributed according to their respective behavioural relevance.

Rock Melon Crop Yield Prediction using Supervised Classification Machine Learning on Cloud Computing

Precision agriculture is a technology-driven approach to farmer to improve their crop yields and reduce costs. One of the major challenges facing farmers today is the lack of precise prediction which leads to decreased production and mismanagement of labour and resource. Precision technology is costly, and they only rely on manual observations which are less precise. Crop yield prediction systems on cloud computing can solve both problems by predicting the harvested fruit at earlier stages of farming and ease farmers to make decisions. In this study, we proposed a crop yield prediction system for farmers that utilizes cloud computing and machine learning techniques. The system uses data on the physical growth of the plant such as plant’s height at 15 and 30 days after transplant, type of pollination treatment, condition of the leaves, and their variety to predict the crop yield at the early stage. Logistic regression, k-nearest neighbour, and random forest classifier were used to compare the accuracy of the model. Our result shows that by using a random forest classifier, it can achieve an accuracy of 91% which is higher than logistic regression which is only 73% of accuracy, and k-nearest neighbour with 82% accuracy. The study highlights the potential of precision agriculture, cloud computing, and machine learning to revolutionize the way farmers manage their crops and increase their efficiency and productivity, even with the limited resources and hardware that many farmers have.

Heart Disease Prediction Using Machine Learning Algorithms

The cases of heart disease increase rapidly day by day and it is essential to predict that type of disease. The prediction is a critical task it must be performed accurately and precisely. This research paper focuses on which patient suffers from cardiovascular disease based on different medical attributes like age, cholesterol level, chest pain types, fasting blood sugar, etc. Taking the medical history of the patient we have created a cardiovascular disease prediction system to predict if the patient is suffering from disease or not. This prediction system is designed by using some of the machine learning algorithms or classifiers such as Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, K-Nearest Neighbor, Logistic Regression, and Gradient Boosting. Before running the model, the data is preprocessed to get the better accuracy. To train the model the dataset is divided into 20% testing and 80% training and achieves accuracy as follows: DT: 83.90%, RF: 88.78%, SVM: 83.90%, NB: 82.43%, K-NN: 95.60%, LR: 83.90% and GB: 86.82%. The prediction accuracy of the K-Nearest Neighbor is higher than the other algorithms. The given prediction system reduces the cost and enhances medical care. This prediction system helps us to predict patients with heart disease and is implemented in Colab .pynb format.

Exploring Machine Learning Approaches for Predicting Lung Cancer: A Comparative Study

Lung cancer is becoming more common, which emphasizes the significance of precise and user-friendly prediction methods for risk assessment and early identification. This research makes use of a dataset (284 cases and 16 attributes) that was acquired from the Online Lung Cancer Prediction System. The main goal is to find out how well machine learning techniques can predict the likelihood of lung cancer and give people useful information for making decisions. The collection contains a wide range of characteristics, such as lifestyle factors, health markers, and demographic data. To thoroughly assess predictive models, the study uses a variety of machine learning methods, including Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Decision Tree, Random Forest, XGBOOST, CATBOOST, LightGBM and Gradient Boosting. The study also investigates the effects of hyperparameter tuning, visualization strategies, and data preprocessing techniques on model performance. Keywords: Cancer prediction· Machine learning· Ensemble models

Soft set-based MSER end-to-end system for occluded scene text detection, recognition and prediction

The presence of unpredictable occlusions on natural scene text is a significant challenge, exacerbating the difficulties already posed on text detection and recognition by the variability of such images. Addressing the need for a robust, consistently performing approach that can effectively address the above challenges, this paper presents a new Soft Set-based end-to-end system for text detection, recognition and prediction in occluded natural scene images. This is the first approach to integrate text detection, recognition and prediction, unlike existing systems developed for end-to-end text spotting (text detection and recognition) only. For candidate text components detection, the proposed combination of Soft Sets with Maximally Stable Extremal Regions (SS-MSER) improves text detection and spotting in natural scene images, irrespectively of the presence of arbitrarily orientated and shaped text, complex backgrounds and occlusion. Furthermore, a Graph Recurrent Neural Network is proposed for grouping candidate text components into text lines and for fitting accurate bounding boxes to each word. Finally, a Convolutional Recurrent Neural Network (CRNN) is proposed for the recognition of text and for predicting missing characters due to occlusion. Experimental results on a new occluded scene text dataset (OSTD) and on the most relevant benchmark natural scene text datasets demonstrate that the proposed system outperforms the state-of-the-art in text detection, recognition and prediction. The code and dataset are available at https://github.com/alloydas/Softset-MSER-Based-Occluded-Scene-Text-Spotting/blob/master/Soft_set_MSER.ipynb

Artificial Intelligence Hybrid Survival Assessment System for Robot-Assisted Proctectomy: A Retrospective Cohort Study.

Robotic-assisted proctectomy (RAP) has emerged as the predominant surgical approach for patients with rectal cancer in recent years; although good postoperative patient recovery with accurate prediction is a guarantee of adaptive surveillance management, there is still a lack of easy-to-use prognostic tools and risk scores designed specifically for those patients undergoing RAP. This study used the electronic health records of 506 RAP participants, including a National Specialist Center for da Vinci Robotic Colorectal Surgery (NSCVRCS) meta cohort, and an independent external validation Sun Yat-sen Memorial Hospital cohort. In the NSCVRCS meta cohort, patients were divided into a discovery cohort (70%, n = 268), where the best-fit model was applied to model our prediction system, RAP-AIscore. Subsequently, an internal validation process for RAP-AIscore was conducted using a replication cohort (30%, n = 116). The study designed and implemented a large-scale artificial intelligence (AI) hybrid framework to identify the best strategy for building a survival assessment system, the RAP-AIscore, from 132 potential modeling scenarios through a combination of iterative cross-validation, Monte Carlo cross-validation, and bootstrap resampling. The 10 variables most relevant to clinical interpretability were identified on the basis of the AI hybrid optimal model values, which helps provide reliable prognostic survival guidance for new patients. The consistent evaluation of discrimination, calibration, generalization, and prognostic value across cohorts reaffirmed the accuracy and robust extrapolation capability of this system. The 10 feature variables most associated with clinical interpretability on the basis of Shapley values were identified, facilitating reliable prognostic survival guidance for new patients. This study introduces a promising and informative tool, the RAP-AIscore, which can be explained through nomograms for interpreting clinical outcomes. It facilitates postoperative risk stratification management and enhances clinical management of prognosis for RAP patients.

Study on Fault Prediction Method of Hydropower Equipment Based on Equipment Health State Detection

Abstract With the continuous development of China’s electric power industry, the installed capacity of power plants is increasing, and hydropower units are developing in the direction of large capacity and high efficiency. As the complexity of the equipment is getting higher and higher, different equipment are more and more different, showing different characteristics when faults occur, which puts forward higher requirements for the safe and stable operation of hydropower units. In response to the above problems, this paper develops a set of human-machine closed-loop remote fault diagnosis reasoning machine system with self-learning function and a set of equipment health state monitoring and prediction system. This remote fault diagnosis system reasoning machine system can reduce the cost of unit equipment fault diagnosis, improve the diagnosis efficiency, and ensure the safe, stable and economic operation of the unit.

ICOPP: A Highway Autonomous Driving Planner Based on Interactive Collaborative Optimization and POMDP

Abstract In this paper, we propose a planner for autonomous vehicles in complex highway scenarios, which can efficiently plan the trajectory of autonomous vehicles. The method aims to improve the intelligence of autonomous driving under the uncertainty of the perception and prediction system. The method employs a POMDP framework to address the challenges brought about by uncertainty. Firstly, this method divides the entire planning cycle into several discrete decision-making processes. In each process, the vehicle’s lateral and longitudinal behaviors are semantically processed to obtain a limited number of decision sequences, thereby addressing the curse of dimensionality in state space and observation space in POMDP problems. In each decision-making process, different simulation scenarios are constructed based on the decision of the self-vehicle and probability distribution of the predicting behaviors of surrounding vehicles. In each scenario, key vehicles are selected for collaborative optimal planning among multiple vehicles, resulting in the optimal drivable trajectories for both the ego vehicle and other vehicles under the given decision. Finally, an evaluation of the policy is conducted in terms of safety, comfort, and efficiency to select the optimal policy. In simulation experiments, the ICOPP demonstrates smoother and more stable autonomous driving performance compared to traditional methods.

A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry

The current research utilized visual characteristics obtained from RGB images and qualitative characteristics to investigate changes in surface defects, predict physical and chemical characteristics, and classify sweet cherries during storage. It was achieved with the help of ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System) models. The ANN used in this study was a Multilayer Perceptron (MLP) with SigmoidAxon and TanhAxon threshold functions, trained with the Momentum training function. Additionally, ANFIS with a Mamdani system and Triangle, Gauss, and Trapezoidal membership functions, was employed to predict sweet cherries' physical and chemical properties and their quality classification. Both models incorporate four algorithms. Additionally, the algorithms use color statistical features and color texture features combined with physical and chemical properties, including weight loss, firmness, titratable acidity, and total anthocyanin content. The image color and texture characteristics were used by ANN and ANFIS models to predict physical and chemical properties with high accuracy. ANN and ANFIS models accurately estimate sweet cherry quality grades in all four algorithms with over 90% accuracy. According to the findings, the ANN and ANFIS models have demonstrated satisfactory performance in the qualitative classification and prediction of sweet cherries' physical and chemical properties.

Early heart disease prediction using feature engineering and machine learning algorithms

Heart disease is one of the most widespread global health issues, it is the reason behind around 32 % of deaths worldwide every year. The early prediction and diagnosis of heart diseases are critical for effective treatment and sickness management. Despite the efforts of healthcare professionals, cardiovascular surgeons and cardiologists' misdiagnosis and misinterpretation of test results may happen every day. This study addresses the growing global health challenge raised by Cardiovascular Diseases (CVDs), which account for 32 % of all deaths worldwide, according to the World Health Organization (WHO). With the progress of Machine Learning (ML) and Deep Learning (DL) techniques as part of Artificial Intelligence (AI), these technologies have become crucial for predicting and diagnosing CVDs. This research aims to develop an ML system for the early prediction of cardiovascular diseases by choosing one of the powerful existing ML algorithms after a deep comparative analysis of several. To achieve this work, the Cleveland and Statlog heart datasets from international platforms are used in this study to evaluate and validate the system's performance. The Cleveland dataset is categorized and used to train various ML algorithms, including decision tree, random forest, support vector machine, logistic regression, adaptive boosting, and K-nearest neighbors. The performance of each algorithm is assessed based on accuracy, precision, recall, F1 score, and the Area Under the Curve metrics. Hyperparameter tuning approaches have been employed to find the best hyperparameters that reflect the optimal performance of the used algorithms based on different evaluation approaches including 10-fold cross-validation with a 95 % confidence interval. The study's findings highlight the potential of ML in improving the early prediction and diagnosis of cardiovascular diseases. By comparing and analyzing the performance of the applied algorithms on both the Cleveland and Statlog heart datasets, this research contributes to the advancement of ML techniques in the medical field. The developed ML system offers a valuable tool for healthcare professionals in the early prediction and diagnosis of cardiovascular diseases, with implications for the prediction and diagnosis of other diseases as well.

Identification and validation of a novel innate lymphoid cell-based signature to predict prognosis and immune response in liver cancer by integrated single-cell RNA analysis and bulk RNA sequencing.

Innate lymphoid cells (ILCs) exert tumor suppressive and tumor promoting effects. However, the prognostic significance of ILC-associated genes remains unclear in hepatocellular carcinoma (HCC). Hence, the aim of this research was to develop an innovative predictive risk classification system using bioinformatics examination. We explored the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to gather data pertaining to HCC and its clinical details. Significantly different ILC-associated genes were investigated by Seurat analysis. The number of signaling interactions of ILCs with other cells was discovered by CellPhoneDB analysis. ClusterProfiler and Metascape were utilized to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on ILC genes. In order to identify potential ILC predictors, we utilized univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses, subsequently validating these predictors in TCGA and GEO groups. The multi-omics ILC signature model's clinical predictive capabilities, along with drug sensitivity and immune factor relations, were assessed using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and pRRophetic. We investigated the possible molecular pathways in our predictive ILC signature through the utilization of gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Five key genes were screened out by constructing a competing endogenous RNA (ceRNA) network using Cytoscape and their values in clinical indexes were demonstrated. Immunohistochemistry (IHC) in HCC cases confirmed the expression of these genes. ILC cell subsets were identified, and cell-cell communication analysis revealed that the signaling pathways involving ILC cell subsets were mostly abundant in the HCC microenvironment. Subsequently, 270 marker genes of the ILC clusters were subjected to GO and KEGG enrichment analysis. Furthermore, a total of 58 prognostically relevant genes were screened as features for prognostic prediction models. Next, the models were validated and clinically evaluated (P values of Kaplan-Meier survival curves below 0.05). Five key genes (C2, STK4, CALM1, IL7R, and RORA) were further screened by multi omics analysis of immune cell and factor and drug sensitivity and correlation analysis of tumor regulatory genes in liver cancer. Furthermore, the potential clinical value of the 5 key genes was confirmed in HCC patients. Finally, the IHC results confirmed the expression of C2, STK4, CALM1, IL7R, and RORA in HCC. Our experimental results provided preliminary evidence supporting the oncogenic roles of STK4 and CALM1, as well as the tumor-suppressive roles of C2, RORA, and IL7R in HCC. A novel prognostic feature of ILC potentially involved in HCC was discovered. It showed high values in predicting patient overall survival (OS) as well as good differences in immunity and drug sensitivity. Therefore, targeting these ILC signatures may be a potential effective approach in HCC treatment.

Predictive value of clinico-radiological variables in the management of suspected foreign body aspiration in children: A ten-year retrospective cohort study

ABSTRACT Background: Definite history is not always present in children with foreign body aspiration (FBA), hence necessitating a high index of suspicion. Objective: To assess the predictive value of clinico-radiological variables among children presenting with features of suspected FBA and to document their course in a tertiary care teaching hospital. Materials and Methods: In this retrospective observational study, we included children aged below 15 years presenting with clinical features of suspected FBA. Data was obtained from case records. Multivariable binary logistic regression analysis (MVA) was performed to determine statistically significant predictors of FBA, and Weighted Risk Scores (WRS) were calculated to determine the significance of the model. Results: Of the 296 children included in the study and having undergone bronchoscopy, 84.5% were toddlers; 269 (90.87%) had a foreign body (FB) removed. Organic material was commonly found with 64.3% being peanut and right main bronchus, the commonest lodging site (50.6%). Of the 54 (18%) children with symptomatic but unwitnessed FBA, 45 (83.3%) had a FB retrieved. Choking with sudden onset cough, stridor, unilateral decreased breath sounds, abnormal chest radiograph, unilateral hyperinflation and mediastinal shift correlated with FBA (P < 0.05). MVA confirmed statistical significance with choking with sudden onset cough to predict FBA (P = 0.024) with overall sensitivity and specificity of 60.9% and 77.8%, respectively. Total WRS with cut-off >2 showed good power of discrimination with sensitivity, specificity and AUC of 60.2%, 77.8% and 0.704%, respectively. Conclusions: The proposed predictive value-based scoring system enhances accuracy in decision-making regarding bronchoscopy in children with suspected FBA.

Landslide Hazard Is Projected to Increase Across High Mountain Asia

AbstractHigh Mountain Asia has long been known as a hotspot for landslide risk, and studies have suggested that landslide hazard is likely to increase in this region over the coming decades. Extreme precipitation may become more frequent, with a nonlinear response relative to increasing global temperatures. However, these changes are geographically varied. This article maps probable changes to landslide hazard, as shown by a landslide hazard indicator (LHI) derived from downscaled precipitation and temperature. In order to capture the nonlinear response of slopes to extreme precipitation, a simple machine‐learning model was trained on a database of landslides across High Mountain Asia to develop a regional LHI. This model was applied to statistically downscaled data from the 30 members of the Seamless System for Prediction and Earth System Research large ensembles to produce a range of possible outcomes under the Shared Socioeconomic Pathways 2‐4.5 and 5‐8.5. The LHI reveals that landslide hazard will increase in most parts of High Mountain Asia. Absolute increases will be highest in already hazardous areas such as the Central Himalaya, but relative change is greatest on the Tibetan Plateau. Even in regions where landslide hazard declines by year 2100, it will increase prior to the mid‐century mark. However, the seasonal cycle of landslide occurrence will not change greatly across High Mountain Asia. Although substantial uncertainty remains in these projections, the overall direction of change seems reliable. These findings highlight the importance of continued analysis to inform disaster risk reduction strategies for stakeholders across High Mountain Asia.

World Futures Day 2023 Global futures agenda by The Millennium Project

World Futures Day (WFD), the 24 h round-the-world participatory method hosted every year by The Millennium Project and others on March 1, stimulates conversations and collective intelligence on possible futures. The 2023 edition marked its tenth anniversary. The 2023 Global futures agenda coming out from the data analysis is composed of four main clusters, each listing future oriented topics and related contents. Cluster 1, Complex solutions for complex problems, includes topics such as Environmental regeneration, Predictable food systems, Energy transition, Homo Galaxia, and Synergetic relations for peace. Cluster 2, A hyper-technological humanity, includes topics such as A biological revolution, New definitions of truth, An emotional AGI, A conscious AGI, and AGI governance. Cluster 3, Education and learning for a better future, includes topics such as Intelligent agent teachers, Intergenerational storytelling, and A cultural shift to self-actualization. Cluster 4, Improving global foresight, includes topics such as Futurists as a contradiction, A new leadership agenda, Futures shapers, The power of stories, Judging trillions of scenarios, Committees and summits for the future. After discussing some changes in the contents addressed over the years and some potential next steps for the research on WFD, the conclusions report that one key takeaway from WFD 2023 is that we have a vital need for pragmatic hope.

Research on Mental Health Assessment and Intervention Methods for College Students based on Big Data Analysis

This study introduces the novel Fuzzy-Enhanced Predictive Neural System for Student Mental Health (FEPS-MH) approach to mental health assessment and intervention for college students. FEPS-MH synergistically combines a Backpropagation (BP) Neural Network with a Deep Fuzzy-Based Neural Network (DFBNN), leveraging the strengths of both systems to handle the complexities of mental health data in the context of big data analytics. The BP Neural Network, known for its effective learning and generalization capabilities, is integrated with the DFBNN to process imprecise, uncertain, or subjective data, typical in mental health assessments. The core objective of FEPS-MH is to provide a more accurate, robust, and sensitive analysis of mental health states, incorporating the nuanced variations and uncertainties inherent in psychological data. This system is designed to analyze a vast array of data sources, including but not limited to, behavioral patterns, self-reported questionnaires, and social media interactions, to identify potential mental health issues among college students. FEPS-MH’s capabilities extend beyond mere assessment; it is also equipped to recommend personalized intervention strategies. Utilizing big data analysis, the system not only predicts potential mental health crises but also suggests tailored intervention approaches based on the unique psychological profile of each student. This study demonstrates the feasibility and effectiveness of FEPS-MH through a series of tests and validations using real-world data. The results indicate a significant improvement in both the accuracy of mental health assessments and the efficacy of suggested interventions. FEPS-MH stands as a promising tool for educational institutions, offering a data-driven, sensitive, and comprehensive approach to student mental health care. Its implementation could revolutionize the field of mental health support in college environments, making it a vital asset for proactive psychological wellness in educational settings.